October 2023
How can the analysis of unstructured, free text data from administrative care records inform long term care policy and practice?
Loneliness and care home entry
Why is this important?
Why is this important?
A systematic review of the impact of loneliness on healthcare utilization found two good-fair quality papers internationally (Smith & Victor, 2021)8:
Production of Welfare (POW) approach. Adapted from Knapp (1984). 11
Three approaches:
“You shall know a word by the company it keeps” (J. R. Firth 1957: 11).18
Results
\[ TP = \text{True Positive} \\ \\ TN = \text{True Negative} \\ \\ FP = \textrm{False Positive} \\ \\ FN = \text{False Negative} \\ \\ \text{recall} = \text{sensitivity} \]
| model type | model | classifier | accuracy | precision | recall | f1 |
|---|---|---|---|---|---|---|
| Transformers | RoBERTa | Neural network | 0.97 | 0.95 | 0.87 | 0.91 |
| Transformers | DistilRoBERTa | Neural network | 0.96 | 0.90 | 0.82 | 0.86 |
| Transformers | MiniLM | Neural network | 0.92 | 0.81 | 0.60 | 0.69 |
| Pre-trained vectors | GloVe | Neural network | 0.90 | 0.78 | 0.50 | 0.61 |
| Word counts | Bag of Words | QDA | 0.27 | 0.15 | 0.83 | 0.26 |
What about the rest of the data?
Results
ELSA
|
Islington
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|---|---|---|---|---|---|---|
| CES-D | UCLA | Assessment | Case notes | Either | Both | |
| Age | 1.01 (0.99-1.03) | 1.00 (0.99-1.02) | 1.01 (0.99-1.02) | 1.01 (1.00-1.03) | 1.01 (1.00-1.02) | 1.01 (1.00-1.03) |
| Dressing | 0.88 (0.62-1.23) | 0.92 (0.66-1.28) | 0.87 (0.78-0.97) ** | 0.91 (0.82-1.01) . | 0.88 (0.79-0.99) * | 0.87 (0.78-0.97) * |
| Ethnicity (non-white) | 1.60 (0.47-5.34) | 1.33 (0.36-5.05) | 0.96 (0.76-1.23) | 0.98 (0.77-1.24) | 0.99 (0.77-1.27) | 0.95 (0.72-1.23) |
| Lives alone | 6.12 (4.36-8.70) *** | 3.57 (2.62-4.90) *** | 1.37 (1.08-1.75) ** | 1.38 (1.09-1.76) ** | 1.52 (1.18-1.95) *** | 1.39 (1.06-1.82) * |
| Meals | 1.36 (0.89-2.07) | 1.31 (0.86-1.99) | 1.02 (0.90-1.16) | 1.09 (0.96-1.23) | 1.09 (0.95-1.24) | 1.04 (0.91-1.19) |
| Memory | 1.14 (0.47-2.71) | 0.65 (0.26-1.57) | 1.40 (1.25-1.57) *** | 1.40 (1.25-1.57) *** | 1.50 (1.33-1.70) *** | 1.47 (1.30-1.67) *** |
| Mobility | 0.86 (0.56-1.32) | 1.19 (0.79-1.80) | 0.89 (0.81-0.99) * | 0.99 (0.90-1.10) | 1.01 (0.90-1.13) | 0.85 (0.76-0.96) ** |
| Safety & risk | 1.35 (0.55-3.26) | 1.17 (0.48-2.85) | 1.03 (0.92-1.15) | 0.94 (0.84-1.05) | 1.00 (0.89-1.12) | 0.96 (0.85-1.09) |
| Sex (F) | 0.92 (0.66-1.27) | 0.99 (0.72-1.35) | 1.15 (0.91-1.45) | 0.89 (0.70-1.12) | 0.97 (0.76-1.24) | 1.06 (0.82-1.37) |
| Shopping | 1.61 (1.13-2.29) ** | 1.45 (1.03-2.05) * | 1.09 (0.93-1.28) | 1.02 (0.88-1.20) | 1.06 (0.90-1.24) | 1.07 (0.90-1.28) |
| Toileting | 1.25 (0.79-1.97) | 1.46 (0.94-2.26) . | 1.04 (0.95-1.15) | 0.88 (0.80-0.97) * | 0.93 (0.84-1.03) | 0.97 (0.87-1.08) |
| Unpaid care | 1.84 (1.08-3.19) * | 2.22 (1.27-3.98) ** | 1.01 (0.78-1.30) | 0.88 (0.68-1.14) | 0.94 (0.71-1.23) | 0.92 (0.70-1.23) |
| *** < 0.001; ** <0.01; * <0.05; . <0.1 | ||||||
Paper 2: Quantifying risk of care home entry
\[h(t | \textbf{X}_i) = h_0(t) \cdot e^{\beta_i \textbf{X}_i}\]
\[ g\{(E(y_{it}) | \textbf{X})\} = \beta_{it} \textbf{X}_{it}, \: \: \: y \sim F \: \textrm{with parameters} \: \theta_{it} \]
Where \(y\) is service cost, \(\textbf{X}\) is a vector of covariates including loneliness and other needs.
| Category | Value | Overall (%) | Care home (%) | p |
|---|---|---|---|---|
| Lonely / Isolated | Lonely/isolated | 380 (29) | 135 (38) | <0.001 |
| Not lonely/isolated | 951 (71) | 221 (62) | ||
| Sex | Female | 826 (62) | 212 (60) | 0.282 |
| Male | 505 (38) | 144 (40) | ||
| Ethnicity | Non-white | 440 (33) | 96 (27) | 0.005 |
| White | 891 (67) | 260 (73) | ||
| Age Group | (60,75] | 208 (16) | 54 (15) | <0.001 |
| (75,80] | 183 (14) | 40 (11) | ||
| (80,85] | 273 (21) | 81 (23) | ||
| (85,90] | 311 (23) | 87 (24) | ||
| (90,95] | 239 (18) | 74 (21) | ||
| (95,110] | 117 (9) | 20 (6) | ||
| Personal Care | Fully independent | 161 (12) | 45 (13) | 0.012 |
| Largely independent | 173 (13) | 86 (24) | ||
| Partial independence | 304 (23) | 69 (19) | ||
| Limited independence | 288 (22) | 78 (22) | ||
| High support needs | 703 (53) | 44 (12) | ||
| Very high support needs | 87 (7) | 34 (10) | ||
| Memory | No issues | 519 (39) | 70 (20) | <0.001 |
| Mild | 354 (27) | 89 (25) | ||
| Some | 269 (20) | 101 (28) | ||
| Marked | 157 (12) | 76 (21) | ||
| Severe | 32 (2) | 20 (6) | ||
| Lives Alone | FALSE | 374 (28) | 154 (43) | 0.399 |
| TRUE | 957 (72) | 202 (57) | ||
| Safety & Risk | Less than daily | 76 (6) | 16 (4) | <0.001 |
| Telecare | 260 (20) | 55 (15) | ||
| Daily | 561 (42) | 121 (34) | ||
| More than daily | 216 (16) | 46 (13) | ||
| Someone nearby | 181 (14) | 99 (28) | ||
| 1to1+ | 37 (3) | 19 (5) | ||
| Unpaid Care | FALSE | 374 (28) | 121 (34) | 0.005 |
| TRUE | 957 (72) | 235 (66) | ||
| Day-to-Day Activities | Largely independent | 173 (13) | 42 (12) | 0.141 |
| Some needs | 455 (34) | 110 (31) | ||
| High support needs | 703 (53) | 204 (57) |
Estimate \(h_k (t)\) for \(k \in \{1,2\}\), where \(1 = \text{care home}, 2 = \text{death}\).
Survival hazard function:22 \[h (t) = \lim_{\Delta\to0} \frac{1}{\Delta(t)} P(t < T \leqslant t + \Delta(t) \: | \: T > t)\]
Cause-specific hazard function:23 \[h^{\color{yellow}{cs}}_k (t) = \lim_{\Delta\to0} \frac{1}{\Delta(t)}P(t < T \leqslant t + \Delta(t), \: \mathbin{\color{yellow}{K = k}} \: | \: T > t)\]
Subdistribution hazard function (Fine & Gray model):24 \[h^{\color{#33FFFF}{sd}}_k (t) = \lim_{\Delta\to0} \frac{1} {\Delta(t)}P(t < T \leqslant t + \Delta(t), \: \color{yellow}{K = k} \: | \\ \: T > t \: \mathbin{\color{#33FFFF}{ \cup \: (T \leqslant t \cap K \neq k) }}\color{white}{})\]
Adapted from Mozmunder (2018) 25
Estimate \(h_k^j (t)\) for \(k \in \{1,2\}\) and \(j \in {1,2}\), where \(k: {1 = \text{care home}, 2 = \text{death}}\) and \(j: {1 = \text{cause-specific}, 2 = \text{subdistribution}}.\) 26 27
\[ h_k^{j} (t | \textbf{X}) = h_{0,k}^{j} \cdot e^{\beta_{i,k}^{j} \textbf{X}_i} \]
stpm2cr.survival::coxph() (cause-specific hazard) and cmprsk::crr() (subdistribution hazard).Preliminary results
Regression model including covariates
Bradshaw, S.A., Playford, E.D. and Riazi, A., 2012. Living well in care homes: a systematic review of qualitative studies. Age and ageing, 41(4), pp.429-440.
World Health Organization, (2021). Social isolation and loneliness among older people: advocacy brief.
Holt-Lunstad, J., Smith, T.B., Baker, M., Harris, T. and Stephenson, D., (2015). Loneliness and social isolation as risk factors for mortality: a meta-analytic review. Perspectives on psychological science, 10(2), pp.227-237.
DCMS: Department for Culture, Media and Sport, (2018). A connected society: a strategy for tackling loneliness, TSO.
World Health Organization, (2021). Social isolation and loneliness among older people: advocacy brief.
Coyle, C.E. and Dugan, E., (2012). Social isolation, loneliness and health among older adults. Journal of aging and health, 24(8), pp.1346-1363.
de Jong-Gierveld, J., (1987). Developing and testing a model of loneliness. Journal of personality and social psychology, 53(1), p.119.
Smith, K.J. and Victor, C., (2021). The Association of Loneliness with Health and Social Care Utilization in Older Adults in the General Population: A Systematic Review. The Gerontologist.
Hanratty, B., Stow, D., Collingridge Moore, D., Valtorta, N.K. and Matthews, F., (2018). Loneliness as a risk factor for care home admission in the English Longitudinal Study of Ageing. Age and ageing, 47(6), pp.896-900.
Russell, D. W., Cutrona, C. E., de la Mora, A., & Wallace, R. B. (1997). Loneliness and nursing home admission among rural older adults. Psychology and aging, 12(4), 574. https://doi.org/10.1037/0882-7974.12.4.574
Martin R J Knapp. The economics of social care. Macmillan International Higher Education, 1984. ISBN 1349177083.
Lagsten, J. and Andersson, A., 2018. Use of information systems in social work–challenges and an agenda for future research. European Journal of Social Work, 21(6), pp.850-862.
Preston-Shoot, Michael, 2019, Analysis of Safeguarding Adult Reviews April 2017 - March 2019, https://www.local.gov.uk/sites/default/files/documents/National%20SAR%20Analysis%20Final%20Report%20WEB.pdf
Farelly, Nicola, 2023 Good practice in digitisation of social care records
Lagsten, J. and Andersson, A., 2018. Use of information systems in social work–challenges and an agenda for future research. European Journal of Social Work, 21(6), pp.850-862.
Preston-Shoot, Michael, 2019, Analysis of Safeguarding Adult Reviews April 2017 - March 2019, https://www.local.gov.uk/sites/default/files/documents/National%20SAR%20Analysis%20Final%20Report%20WEB.pdf
Farelly, Nicola, 2023 Good practice in digitisation of social care records
Firth, J.R., 1957. A synopsis of linguistic theory, 1930-1955. Studies in linguistic analysis.
Pennington, J., Socher, R. and Manning, C.D., (2014), October. Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532-1543).
Pennington, J., Socher, R. and Manning, C.D., (2014), October. Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532-1543).
Pennington, J., Socher, R. and Manning, C.D., (2014), October. Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532-1543).
Austin, P.C., Lee, D.S. and Fine, J.P., 2016. Introduction to the analysis of survival data in the presence of competing risks. Circulation, 133(6), pp.601-609. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4741409/
Austin, P.C., Lee, D.S. and Fine, J.P., 2016. Introduction to the analysis of survival data in the presence of competing risks. Circulation, 133(6), pp.601-609. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4741409/
Mozumder, S.I., Rutherford, M.J. and Lambert, P.C., 2017. A flexible parametric competing-risks model using a direct likelihood approach for the cause-specific cumulative incidence function. The Stata Journal, 17(2), pp.462-489.
Mozumder, S.I., Analysing competing risks data using flexible parametric survival models: what tools are available e in Stata, which ones to use and when?, 2018 London Stata Conference | 6 - 7 September 2018, https://www.stata.com/meeting/uk18/slides/uk18_Mozumder.pdf
Mozumder, S.I., Rutherford, M.J. and Lambert, P.C., 2017. A flexible parametric competing-risks model using a direct likelihood approach for the cause-specific cumulative incidence function. The Stata Journal, 17(2), pp.462-489.
Prentice, R.L., Kalbfleisch, J.D., Peterson Jr, A.V., Flournoy, N., Farewell, V.T. and Breslow, N.E., 1978. The analysis of failure times in the presence of competing risks. Biometrics, pp.541-554.